Many bridge design specifications consider multi-lane factors(MLFs)a critical component of the traffic load model.Measured multi-lane traffic data generally exhibit significant lane disparities in traffic loads over m...Many bridge design specifications consider multi-lane factors(MLFs)a critical component of the traffic load model.Measured multi-lane traffic data generally exhibit significant lane disparities in traffic loads over multiple lanes.However,these disparities are not considered in current specifications.To address this drawback,a multi-coefficient MLF model was developed based on an improved probabilistic statistical approach that considers the presence of multiple trucks.The proposed MLF model and approach were calibrated and demonstrated through an example site.The model sensitivity analysis demonstrated the significant influence of lane disparity of truck traffic volume and truck weight distribution on the MLF.Using the proposed approach,the experimental site study yielded MLFs comparable with those directly calculated using traffic load effects.The exclusion of overloaded trucks caused the proposed approach,existing design specifications,and conventional approach of ignoring lane load disparity to generate comparable MLFs,while the MLFs based on the proposed approach were the most comprehensive.The inclusion of overloaded trucks caused the conventional approach and design specifications to overestimate the MLFs significantly.Finally,the benefits of the research results to bridge practitioners were discussed.展开更多
Existing studies on modern roundabouts performance are mostly based on data fron： singe lane roundabouts that are not heavily congested. For planners and designers interested in building multilane roundabouts for int...Existing studies on modern roundabouts performance are mostly based on data fron： singe lane roundabouts that are not heavily congested. For planners and designers interested in building multilane roundabouts for intersections with potential growth i~ future traffic, there has been a lack of existing studies with field data that provide reference values in terms of capacity and delay measurements. With the intent of providing such reference values, a case study was conducted by using the East DowlinC Road Roundabouts in Anchorage, Alaska, which are currently operating with extensive queues during the evening peak hours. This research used multiple video camcorders t（ capture vehicle turning movements at the roundabouts as well as the progressior~ of vehicle queues at the roundabout entrance approaches. With these video records, the number of vehicles in the queues can be accurately counted in any single minute during the peak hours. This study shows that unbalanced entrance flow patterns （i.e., ~ne entrance has significant higher flow than others） can intensify the queue and delay fo., the overall roundabouts. Then various software packages including RODEL, SIDRA and VISSIM were used to estimate several performance measurements, such as capacity. queue length, and delay, compared with the collected field data. With the comparison, it is found that all the three software packages overestimate multi-lane roundabout ca pacity before calibration. With default parameters, SIDRA and VISSIM tend to underes timate delays and queue lengths for the multi-lane roundabouts under congestion, while RODEL results in higher delay and queue length estimations at most of the entrance approaches.展开更多
针对现有车道检测算法准确性和实时性较难平衡的问题,提出了一种基于多特征融合和窗口搜索的新型车道线检测算法.采用多边形填充方法确定车道线的感兴趣区域(region of interest, ROI),融合车道线的颜色、直方图和梯度特征,以消除ROI中...针对现有车道检测算法准确性和实时性较难平衡的问题,提出了一种基于多特征融合和窗口搜索的新型车道线检测算法.采用多边形填充方法确定车道线的感兴趣区域(region of interest, ROI),融合车道线的颜色、直方图和梯度特征,以消除ROI中的复杂背景.通过单应性变换得到车道线的二值图像,基于其像素密度分布寻找车道线初始位置,以窗口搜索方式提取整个车道线上的所有候选像素点.通过拟合像素点构建车道线数学模型.结果表明:提出的算法具有较高的准确性和实时性,算法对黄色车道线、树木阴影遮挡、光照变化、车道线缺损和地面交通标志干扰具有较好的鲁棒性.展开更多
为了解决目前车道线检测过程中特征融合不充分、检测精确度低和鲁棒性差的问题,本文提出一种融合多分支结构和注意力机制的车道线检测模型(fusion of multi-branch structure and attention mechanism network,FMANet),图像编码部分采...为了解决目前车道线检测过程中特征融合不充分、检测精确度低和鲁棒性差的问题,本文提出一种融合多分支结构和注意力机制的车道线检测模型(fusion of multi-branch structure and attention mechanism network,FMANet),图像编码部分采用多分支结构和注意力机制,并选择swish作为激活函数,图像解码部分采用跳跃连接结构,实现跨层特征融合。本文利用TuSimple公开数据集对FMANet模型进行评估与验证,实验结果表明,本文所提的FMANet模型的mAP指标接近97.25%,车道线检测精确度达到98.15%,此外,通过CULane数据集验证FMANet模型在不同场景下的检测具有更好的鲁棒性。展开更多
基金This work was supported by the National Natural Science Foundation of China(Grant No.51808148)Natural Science Foundation of Guangdong Province,China(No.2019A1515010701)Guangzhou Municipal Science and Technology Project(No.201904010188).
文摘Many bridge design specifications consider multi-lane factors(MLFs)a critical component of the traffic load model.Measured multi-lane traffic data generally exhibit significant lane disparities in traffic loads over multiple lanes.However,these disparities are not considered in current specifications.To address this drawback,a multi-coefficient MLF model was developed based on an improved probabilistic statistical approach that considers the presence of multiple trucks.The proposed MLF model and approach were calibrated and demonstrated through an example site.The model sensitivity analysis demonstrated the significant influence of lane disparity of truck traffic volume and truck weight distribution on the MLF.Using the proposed approach,the experimental site study yielded MLFs comparable with those directly calculated using traffic load effects.The exclusion of overloaded trucks caused the proposed approach,existing design specifications,and conventional approach of ignoring lane load disparity to generate comparable MLFs,while the MLFs based on the proposed approach were the most comprehensive.The inclusion of overloaded trucks caused the conventional approach and design specifications to overestimate the MLFs significantly.Finally,the benefits of the research results to bridge practitioners were discussed.
基金sponsored by Alaska University Transportation Center(AUTC,No.RR08.08)Alaska Department of Transportation(AK DOT)
文摘Existing studies on modern roundabouts performance are mostly based on data fron： singe lane roundabouts that are not heavily congested. For planners and designers interested in building multilane roundabouts for intersections with potential growth i~ future traffic, there has been a lack of existing studies with field data that provide reference values in terms of capacity and delay measurements. With the intent of providing such reference values, a case study was conducted by using the East DowlinC Road Roundabouts in Anchorage, Alaska, which are currently operating with extensive queues during the evening peak hours. This research used multiple video camcorders t（ capture vehicle turning movements at the roundabouts as well as the progressior~ of vehicle queues at the roundabout entrance approaches. With these video records, the number of vehicles in the queues can be accurately counted in any single minute during the peak hours. This study shows that unbalanced entrance flow patterns （i.e., ~ne entrance has significant higher flow than others） can intensify the queue and delay fo., the overall roundabouts. Then various software packages including RODEL, SIDRA and VISSIM were used to estimate several performance measurements, such as capacity. queue length, and delay, compared with the collected field data. With the comparison, it is found that all the three software packages overestimate multi-lane roundabout ca pacity before calibration. With default parameters, SIDRA and VISSIM tend to underes timate delays and queue lengths for the multi-lane roundabouts under congestion, while RODEL results in higher delay and queue length estimations at most of the entrance approaches.
文摘针对现有车道检测算法准确性和实时性较难平衡的问题,提出了一种基于多特征融合和窗口搜索的新型车道线检测算法.采用多边形填充方法确定车道线的感兴趣区域(region of interest, ROI),融合车道线的颜色、直方图和梯度特征,以消除ROI中的复杂背景.通过单应性变换得到车道线的二值图像,基于其像素密度分布寻找车道线初始位置,以窗口搜索方式提取整个车道线上的所有候选像素点.通过拟合像素点构建车道线数学模型.结果表明:提出的算法具有较高的准确性和实时性,算法对黄色车道线、树木阴影遮挡、光照变化、车道线缺损和地面交通标志干扰具有较好的鲁棒性.
文摘为了解决目前车道线检测过程中特征融合不充分、检测精确度低和鲁棒性差的问题,本文提出一种融合多分支结构和注意力机制的车道线检测模型(fusion of multi-branch structure and attention mechanism network,FMANet),图像编码部分采用多分支结构和注意力机制,并选择swish作为激活函数,图像解码部分采用跳跃连接结构,实现跨层特征融合。本文利用TuSimple公开数据集对FMANet模型进行评估与验证,实验结果表明,本文所提的FMANet模型的mAP指标接近97.25%,车道线检测精确度达到98.15%,此外,通过CULane数据集验证FMANet模型在不同场景下的检测具有更好的鲁棒性。
